Ikewete, I (2020) A RISK-BASED VERIFICATION FRAMEWORK FOR OFFSHORE WIND FARM DEVELOPMENT: DESIGN, INSTALLATION, OPERATIONS AND MAINTENANCE OF OFFSHORE WIND TURBINES. Doctoral thesis, Liverpool John Moores University.

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Abstract

This thesis encompasses a holistic review of the development trends in wind turbine technology (onshore and offshore) and the challenges perceived at the stages of design, construction and operations of modern-day wind energy technology (Friedrich and Lukas, 2017). The main focus of this study is to evaluate the risks associated with offshore wind farm development (OWFD). This is achieved by first estimating those perceived risks, understanding the relative importance of each individual risk, and carrying out an assessment using a specialist analytical tool known as AHiP-Evi. AHiP-Evi was developed through a combination of application of Analytic Hierarchy Process (AHP) and Evidential Reasoning (ER) techniques. The AHP was used to ascertain the weighting of the respective risk variables according to their relative importance, while the ER was used to evaluate the aggregated influence of the collective risk variables associated with the OWFD. Finally, a specific modelling tool known as BN-SAT (Bayesian Network Sensitivity Analysis Technique) was developed to evaluate the probabilities of occurrence of the variable nodes and their overall impacts on the decision node (OWFD). The BN-SAT is comprised of a combination of Bayesian networks (BNs) concepts and a sensitivity analysis (SA) approach. The AHiP-Evi model initially developed in this study is transformed into the BN structure in order to compute the conditional and unconditional prior probability for each starting node using the NETICA analytical software to determine the aggregated impact of the specific risk variables on the OWFD. The outcome from this modelling analysis is then compared to the initial assessment carried out by the application of the AHiP-Evi modelling tool in order to validate the robustness of both modelling tools. In the case study of this research, the percentage difference of the outcomes of the two models is insignificant, which demonstrates the fact that both systems are effective. The Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were integrated to develop a specific model for the selection of best-case risk management technique (RMT). Based on the decision makers’ (DMs) aggregated judgements, it was possible to compute the values and determine the best-case RMT dependent on the decision variables driving the decision - for example, costs and benefits, through the developed integrated model known as FAHP-FTOPSIS. The outcome of this selection model has been seen to be reasonably practical and a successful conclusion of the research contribution. Awareness of the aggregated impact of the risk variables is important in making the decision about appropriate investments in a strategic improvement of risk management and efficient resource allocations to the offshore wind industry.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Wind farm; Offshore wind farm; Onshore wind farm; Wind turrbines; Analytical Hierarchy Process; Evidential Reasoning; Bayesian Networts; TOPSIS; FAHP; FTOPSIS; Wind Energy Historical Trends; Historical Accidents and Incidents of OWF; Risk Modelling Techniques; Qualitative analysis; Quantitative analysis; Fuzzy set modelling; Fuzzy rule-based approach; Mapping for the Transformation Process; Fuzzy judgement using a pairwise comparison matrix; Expected Utility Modelling Approach; Sensitivity Analysis; D-Separation; Interrelationships between critical risk factors; Key influencing risk factors; Fuzzy MCDM problem formulation; Decision-making alternatives; Risk-Based Framework
Subjects:	T Technology > T Technology (General) T Technology > TC Hydraulic engineering. Ocean engineering T Technology > TD Environmental technology. Sanitary engineering
Divisions:	Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Date Deposited:	17 Jun 2020 22:26
Last Modified:	08 Nov 2022 14:25
DOI or ID number:	10.24377/LJMU.t.00013090
Supervisors:	Allanson, DR, Wang, J and Blanco Davis, E
URI:	https://researchonline.ljmu.ac.uk/id/eprint/13090

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